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Overparameterized networks are thermodynamically driven toward smooth, data-adaptive solutions by a function-space fluctuation potential that penalizes misalignment between dynamic mobility and parameter-space curvature.
Low generative perplexity in diffusion models often masks excessive repetition, with a simple fix cutting repetition to human levels while being 1.5–5x cheaper.
